class_docstr: The DDRNet implementation based on PaddlePaddle.
  
  The original article refers to
  Yuanduo Hong, Huihui Pan, Weichao Sun, et al. "Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes"
  (https://arxiv.org/abs/2101.06085)
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): Number of input channels. Default: 3.
      block_layers (list, tuple): The numbers of layers in different blocks. Default: [2, 2, 2, 2].
      planes (int): Base channels in network. Default: 64.
      spp_planes (int): Branch channels for DAPPM. Default: 128.
      head_planes (int): Mid channels of segmentation head. Default: 128.
      enable_auxiliary_loss (bool): Whether use auxiliary head for stage3. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
      else:
          for m in self.sublayers():
              if isinstance(m, nn.Conv2D):
                  param_init.kaiming_normal_init(m.weight)
              elif isinstance(m, nn.BatchNorm2D):
                  param_init.constant_init(m.weight, value=1)
                  param_init.constant_init(m.bias, value=0)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the DualResNet obj's underlying member method code with example usage: obj.init_weight() without return types